Overview

Dataset statistics

Number of variables11
Number of observations2771159
Missing cells0
Missing cells (%)0.0%
Duplicate rows38
Duplicate rows (%)< 0.1%
Total size in memory232.6 MiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

Dataset has 38 (< 0.1%) duplicate rowsDuplicates
u is highly correlated with g and 8 other fieldsHigh correlation
g is highly correlated with u and 8 other fieldsHigh correlation
r is highly correlated with u and 8 other fieldsHigh correlation
i is highly correlated with u and 8 other fieldsHigh correlation
z is highly correlated with u and 8 other fieldsHigh correlation
uErr is highly correlated with u and 8 other fieldsHigh correlation
gErr is highly correlated with u and 8 other fieldsHigh correlation
rErr is highly correlated with u and 8 other fieldsHigh correlation
iErr is highly correlated with u and 8 other fieldsHigh correlation
zErr is highly correlated with u and 8 other fieldsHigh correlation
u is highly correlated with g and 2 other fieldsHigh correlation
g is highly correlated with u and 4 other fieldsHigh correlation
r is highly correlated with rErrHigh correlation
i is highly correlated with iErrHigh correlation
z is highly correlated with g and 2 other fieldsHigh correlation
uErr is highly correlated with u and 2 other fieldsHigh correlation
gErr is highly correlated with u and 4 other fieldsHigh correlation
rErr is highly correlated with rHigh correlation
iErr is highly correlated with iHigh correlation
zErr is highly correlated with g and 2 other fieldsHigh correlation
u is highly correlated with g and 4 other fieldsHigh correlation
g is highly correlated with u and 8 other fieldsHigh correlation
r is highly correlated with u and 7 other fieldsHigh correlation
i is highly correlated with g and 6 other fieldsHigh correlation
z is highly correlated with g and 6 other fieldsHigh correlation
uErr is highly correlated with u and 2 other fieldsHigh correlation
gErr is highly correlated with u and 8 other fieldsHigh correlation
rErr is highly correlated with u and 7 other fieldsHigh correlation
iErr is highly correlated with g and 6 other fieldsHigh correlation
zErr is highly correlated with g and 6 other fieldsHigh correlation
u is highly skewed (γ1 = -398.6854988) Skewed
g is highly skewed (γ1 = -424.5365597) Skewed
i is highly skewed (γ1 = -666.5307062) Skewed
z is highly skewed (γ1 = -476.0855038) Skewed
uErr is highly skewed (γ1 = -403.3646815) Skewed
gErr is highly skewed (γ1 = -429.7716532) Skewed
iErr is highly skewed (γ1 = -679.5562007) Skewed
zErr is highly skewed (γ1 = -480.4752825) Skewed

Reproduction

Analysis started2022-02-27 19:50:53.897983
Analysis finished2022-02-27 19:54:24.488465
Duration3 minutes and 30.59 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

ID
Real number (ℝ≥0)

Distinct2771121
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.237664767 × 1018
Minimum1.23764588 × 1018
Maximum1.237680531 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2022-02-27T16:54:24.542948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.23764588 × 1018
5-th percentile1.237651538 × 1018
Q11.237658613 × 1018
median1.237663784 × 1018
Q31.237668298 × 1018
95-th percentile1.237679541 × 1018
Maximum1.237680531 × 1018
Range3.465180558 × 1013
Interquartile range (IQR)9.685164556 × 1012

Descriptive statistics

Standard deviation8.395171953 × 1012
Coefficient of variation (CV)6.783074202 × 10-6
Kurtosis-0.5908909818
Mean1.237664767 × 1018
Median Absolute Deviation (MAD)4.663256416 × 1012
Skewness0.3696698272
Sum-3.735202359 × 1017
Variance7.047891212 × 1025
MonotonicityNot monotonic
2022-02-27T16:54:24.625237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.237657072 × 10182
 
< 0.1%
1.237658612 × 10182
 
< 0.1%
1.23765707 × 10182
 
< 0.1%
1.237663239 × 10182
 
< 0.1%
1.237671765 × 10182
 
< 0.1%
1.237678663 × 10182
 
< 0.1%
1.237663783 × 10182
 
< 0.1%
1.23766533 × 10182
 
< 0.1%
1.237657858 × 10182
 
< 0.1%
1.237663783 × 10182
 
< 0.1%
Other values (2771111)2771139
> 99.9%
ValueCountFrequency (%)
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
ValueCountFrequency (%)
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%

u
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct862299
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.48569117
Minimum-9999
Maximum33.45042
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)< 0.1%
Memory size21.1 MiB
2022-02-27T16:54:24.734610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile18.743918
Q120.68278
median22.81066
Q324.18339
95-th percentile26.06422
Maximum33.45042
Range10032.45042
Interquartile range (IQR)3.50061

Descriptive statistics

Standard deviation24.92592762
Coefficient of variation (CV)1.108523969
Kurtosis160288.9449
Mean22.48569117
Median Absolute Deviation (MAD)1.62966
Skewness-398.6854988
Sum62311425.46
Variance621.3018678
MonotonicityNot monotonic
2022-02-27T16:54:24.828363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.63466363
 
< 0.1%
24.63467315
 
< 0.1%
24.63465259
 
< 0.1%
24.63468224
 
< 0.1%
24.63469101
 
< 0.1%
24.634773
 
< 0.1%
24.6346471
 
< 0.1%
24.6347132
 
< 0.1%
24.6346324
 
< 0.1%
24.6347222
 
< 0.1%
Other values (862289)2769675
99.9%
ValueCountFrequency (%)
-999917
< 0.1%
7.9180761
 
< 0.1%
9.8665341
 
< 0.1%
9.9419421
 
< 0.1%
10.170251
 
< 0.1%
10.227171
 
< 0.1%
10.488951
 
< 0.1%
10.541811
 
< 0.1%
11.06431
 
< 0.1%
11.213971
 
< 0.1%
ValueCountFrequency (%)
33.450421
< 0.1%
32.666631
< 0.1%
31.771321
< 0.1%
31.386641
< 0.1%
31.107921
< 0.1%
30.961
< 0.1%
30.813441
< 0.1%
30.779911
< 0.1%
30.773581
< 0.1%
30.760491
< 0.1%

g
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct771110
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.80203725
Minimum-9999
Maximum33.72469
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)< 0.1%
Memory size21.1 MiB
2022-02-27T16:54:24.928225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile17.24236
Q118.79789
median21.54917
Q322.42595
95-th percentile23.56188
Maximum33.72469
Range10032.72469
Interquartile range (IQR)3.62806

Descriptive statistics

Standard deviation23.40800036
Coefficient of variation (CV)1.125274418
Kurtosis181720.1326
Mean20.80203725
Median Absolute Deviation (MAD)1.22679
Skewness-424.5365597
Sum57645752.73
Variance547.9344807
MonotonicityNot monotonic
2022-02-27T16:54:25.021988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.11438108
 
< 0.1%
25.1143766
 
< 0.1%
25.1143957
 
< 0.1%
25.114445
 
< 0.1%
22.256925
 
< 0.1%
22.1497725
 
< 0.1%
22.0357224
 
< 0.1%
22.2307123
 
< 0.1%
22.1995623
 
< 0.1%
22.1149323
 
< 0.1%
Other values (771100)2770740
> 99.9%
ValueCountFrequency (%)
-999915
< 0.1%
7.4669971
 
< 0.1%
9.8970961
 
< 0.1%
10.246591
 
< 0.1%
10.327731
 
< 0.1%
10.407441
 
< 0.1%
10.533391
 
< 0.1%
10.640631
 
< 0.1%
10.754631
 
< 0.1%
11.100781
 
< 0.1%
ValueCountFrequency (%)
33.724691
< 0.1%
32.909441
< 0.1%
32.149971
< 0.1%
31.670361
< 0.1%
31.602241
< 0.1%
31.523151
< 0.1%
31.354171
< 0.1%
31.327361
< 0.1%
31.066181
< 0.1%
30.953071
< 0.1%

r
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct701820
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.52903535
Minimum8.902843
Maximum22.99995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2022-02-27T16:54:25.115738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8.902843
5-th percentile16.4361
Q117.74938
median20.05903
Q320.9397
95-th percentile22.10703
Maximum22.99995
Range14.097107
Interquartile range (IQR)3.19032

Descriptive statistics

Standard deviation1.868180367
Coefficient of variation (CV)0.09566168188
Kurtosis-0.7443412122
Mean19.52903535
Median Absolute Deviation (MAD)1.34907
Skewness-0.4281030079
Sum54118062.08
Variance3.490097884
MonotonicityNot monotonic
2022-02-27T16:54:25.209488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.550723
 
< 0.1%
20.2904522
 
< 0.1%
20.5256322
 
< 0.1%
20.5042522
 
< 0.1%
20.6365822
 
< 0.1%
20.7846122
 
< 0.1%
20.4497522
 
< 0.1%
20.4366422
 
< 0.1%
20.6164622
 
< 0.1%
20.7174522
 
< 0.1%
Other values (701810)2770938
> 99.9%
ValueCountFrequency (%)
8.9028431
< 0.1%
9.4744761
< 0.1%
9.5015741
< 0.1%
9.8482581
< 0.1%
9.9037461
< 0.1%
9.9209511
< 0.1%
10.044621
< 0.1%
10.072471
< 0.1%
10.109561
< 0.1%
10.131251
< 0.1%
ValueCountFrequency (%)
22.999951
< 0.1%
22.999941
< 0.1%
22.999932
< 0.1%
22.999911
< 0.1%
22.99991
< 0.1%
22.999812
< 0.1%
22.99981
< 0.1%
22.999731
< 0.1%
22.999711
< 0.1%
22.999681
< 0.1%

i
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct684993
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.77579059
Minimum-9999
Maximum31.65274
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)< 0.1%
Memory size21.1 MiB
2022-02-27T16:54:25.303238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile16.02066
Q117.31647
median19.17877
Q319.91381
95-th percentile21.405591
Maximum31.65274
Range10030.65274
Interquartile range (IQR)2.59734

Descriptive statistics

Standard deviation14.83638504
Coefficient of variation (CV)0.7901869685
Kurtosis450050.0913
Mean18.77579059
Median Absolute Deviation (MAD)1.14519
Skewness-666.5307062
Sum52030701.06
Variance220.1183212
MonotonicityNot monotonic
2022-02-27T16:54:25.396977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.361878
 
< 0.1%
24.3618147
 
< 0.1%
19.7492827
 
< 0.1%
19.52326
 
< 0.1%
19.6128326
 
< 0.1%
19.6674526
 
< 0.1%
19.5657325
 
< 0.1%
19.5481925
 
< 0.1%
19.644525
 
< 0.1%
19.6903725
 
< 0.1%
Other values (684983)2770829
> 99.9%
ValueCountFrequency (%)
-99996
< 0.1%
8.3649651
 
< 0.1%
8.4112851
 
< 0.1%
9.3708691
 
< 0.1%
9.5270821
 
< 0.1%
9.5500071
 
< 0.1%
9.5687771
 
< 0.1%
9.754041
 
< 0.1%
9.850521
 
< 0.1%
9.8860051
 
< 0.1%
ValueCountFrequency (%)
31.652741
< 0.1%
31.231631
< 0.1%
31.149821
< 0.1%
31.073351
< 0.1%
30.846291
< 0.1%
30.831511
< 0.1%
30.711051
< 0.1%
30.652091
< 0.1%
30.576231
< 0.1%
30.498611
< 0.1%

z
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct683964
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.35172229
Minimum-9999
Maximum30.01704
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)< 0.1%
Memory size21.1 MiB
2022-02-27T16:54:25.495362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile15.702359
Q117.02602
median18.72145
Q319.44232
95-th percentile21.12194
Maximum30.01704
Range10029.01704
Interquartile range (IQR)2.4163

Descriptive statistics

Standard deviation20.91051379
Coefficient of variation (CV)1.139430592
Kurtosis228069.9466
Mean18.35172229
Median Absolute Deviation (MAD)1.01414
Skewness-476.0855038
Sum50855540.38
Variance437.2495869
MonotonicityNot monotonic
2022-02-27T16:54:25.589112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.8269569
 
< 0.1%
22.82691330
 
< 0.1%
22.82689170
 
< 0.1%
22.8269289
 
< 0.1%
22.8269341
 
< 0.1%
22.8269431
 
< 0.1%
19.1792929
 
< 0.1%
19.2534428
 
< 0.1%
19.2171628
 
< 0.1%
19.1566227
 
< 0.1%
Other values (683954)2769817
> 99.9%
ValueCountFrequency (%)
-999912
< 0.1%
6.4855861
 
< 0.1%
6.9728551
 
< 0.1%
7.1403881
 
< 0.1%
7.2817191
 
< 0.1%
9.5631221
 
< 0.1%
9.6733111
 
< 0.1%
9.7591461
 
< 0.1%
9.7711581
 
< 0.1%
9.9718491
 
< 0.1%
ValueCountFrequency (%)
30.017041
< 0.1%
29.383741
< 0.1%
29.263141
< 0.1%
29.183741
< 0.1%
29.174081
< 0.1%
29.041691
< 0.1%
29.039351
< 0.1%
29.01941
< 0.1%
28.966261
< 0.1%
28.917931
< 0.1%

uErr
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2426286
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6182142492
Minimum-9999
Maximum68.59662
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)< 0.1%
Memory size21.1 MiB
2022-02-27T16:54:25.682862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.034035557
Q10.1267441
median0.5586334
Q31.039912
95-th percentile1.7884133
Maximum68.59662
Range10067.59662
Interquartile range (IQR)0.9131679

Descriptive statistics

Standard deviation24.77498648
Coefficient of variation (CV)40.07508161
Kurtosis162802.3021
Mean0.6182142492
Median Absolute Deviation (MAD)0.447231
Skewness-403.3646815
Sum1713169.98
Variance613.7999553
MonotonicityNot monotonic
2022-02-27T16:54:25.776612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999917
 
< 0.1%
1.208498
 
< 0.1%
1.1366498
 
< 0.1%
1.0697638
 
< 0.1%
1.0185938
 
< 0.1%
1.0406248
 
< 0.1%
1.1518958
 
< 0.1%
1.1764178
 
< 0.1%
1.1608017
 
< 0.1%
1.2408777
 
< 0.1%
Other values (2426276)2771072
> 99.9%
ValueCountFrequency (%)
-999917
< 0.1%
0.00015272191
 
< 0.1%
0.00025310871
 
< 0.1%
0.00059578531
 
< 0.1%
0.00063232911
 
< 0.1%
0.0013525921
 
< 0.1%
0.0015262641
 
< 0.1%
0.0021296481
 
< 0.1%
0.0022865361
 
< 0.1%
0.0024110081
 
< 0.1%
ValueCountFrequency (%)
68.596621
< 0.1%
55.509481
< 0.1%
46.498841
< 0.1%
40.798731
< 0.1%
39.446211
< 0.1%
38.201951
< 0.1%
36.878481
< 0.1%
30.99551
< 0.1%
28.227461
< 0.1%
27.371091
< 0.1%

gErr
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2400043
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08109698909
Minimum-9999
Maximum41.14543
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)< 0.1%
Memory size21.1 MiB
2022-02-27T16:54:25.885987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.0055363294
Q10.013174905
median0.09019347
Q30.17298095
95-th percentile0.4513975
Maximum41.14543
Range10040.14543
Interquartile range (IQR)0.159806045

Descriptive statistics

Standard deviation23.26435021
Coefficient of variation (CV)286.8707023
Kurtosis184714.2977
Mean0.08109698909
Median Absolute Deviation (MAD)0.07794734
Skewness-429.7716532
Sum224732.6512
Variance541.2299908
MonotonicityNot monotonic
2022-02-27T16:54:25.984024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999915
 
< 0.1%
0.107947
 
< 0.1%
0.1281867
 
< 0.1%
0.14900337
 
< 0.1%
0.12248727
 
< 0.1%
0.142687
 
< 0.1%
0.11481877
 
< 0.1%
0.1154497
 
< 0.1%
0.10880427
 
< 0.1%
0.12239377
 
< 0.1%
Other values (2400033)2771081
> 99.9%
ValueCountFrequency (%)
-999915
< 0.1%
0.00020417921
 
< 0.1%
0.0003442481
 
< 0.1%
0.00037442561
 
< 0.1%
0.00041113421
 
< 0.1%
0.00045212181
 
< 0.1%
0.00051167571
 
< 0.1%
0.00052219471
 
< 0.1%
0.00054079661
 
< 0.1%
0.0005696471
 
< 0.1%
ValueCountFrequency (%)
41.145431
< 0.1%
40.807771
< 0.1%
27.876591
< 0.1%
25.203171
< 0.1%
18.666031
< 0.1%
16.810181
< 0.1%
14.33141
< 0.1%
13.896031
< 0.1%
13.569331
< 0.1%
12.596771
< 0.1%

rErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2512735
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05601125719
Minimum0.0002880982
Maximum14.52429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2022-02-27T16:54:26.077168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0002880982
5-th percentile0.004357583
Q10.008711278
median0.03777241
Q30.073950285
95-th percentile0.18078481
Maximum14.52429
Range14.5240019
Interquartile range (IQR)0.065239007

Descriptive statistics

Standard deviation0.06815645868
Coefficient of variation (CV)1.21683501
Kurtosis1299.621293
Mean0.05601125719
Median Absolute Deviation (MAD)0.029996825
Skewness11.73242203
Sum155216.0995
Variance0.00464530286
MonotonicityNot monotonic
2022-02-27T16:54:26.170917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10173526
 
< 0.1%
0.15797216
 
< 0.1%
0.10848296
 
< 0.1%
0.10314316
 
< 0.1%
0.12780866
 
< 0.1%
0.10304226
 
< 0.1%
0.10091136
 
< 0.1%
0.10297356
 
< 0.1%
0.13498716
 
< 0.1%
0.046955466
 
< 0.1%
Other values (2512725)2771099
> 99.9%
ValueCountFrequency (%)
0.00028809821
< 0.1%
0.00029452961
< 0.1%
0.00032062281
< 0.1%
0.00035198211
< 0.1%
0.00037071751
< 0.1%
0.00037074071
< 0.1%
0.00037470931
< 0.1%
0.00038703781
< 0.1%
0.00041203841
< 0.1%
0.00048491931
< 0.1%
ValueCountFrequency (%)
14.524291
< 0.1%
9.040691
< 0.1%
8.5569411
< 0.1%
8.4200181
< 0.1%
7.7123911
< 0.1%
7.4970261
< 0.1%
7.2769151
< 0.1%
5.5287411
< 0.1%
5.5018441
< 0.1%
5.1243161
< 0.1%

iErr
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2458989
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0190022825
Minimum-9999
Maximum55.15096
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)< 0.1%
Memory size21.1 MiB
2022-02-27T16:54:26.264668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.0043685914
Q10.008751204
median0.02717291
Q30.04588176
95-th percentile0.1377873
Maximum55.15096
Range10054.15096
Interquartile range (IQR)0.037130556

Descriptive statistics

Standard deviation14.71337641
Coefficient of variation (CV)774.2952151
Kurtosis461815.2536
Mean0.0190022825
Median Absolute Deviation (MAD)0.018490926
Skewness-679.5562007
Sum52658.34617
Variance216.4834455
MonotonicityNot monotonic
2022-02-27T16:54:26.358408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02938737
 
< 0.1%
0.026033476
 
< 0.1%
0.010299266
 
< 0.1%
0.030170656
 
< 0.1%
0.028515476
 
< 0.1%
0.010496116
 
< 0.1%
-99996
 
< 0.1%
0.01190096
 
< 0.1%
0.011324526
 
< 0.1%
0.034087776
 
< 0.1%
Other values (2458979)2771098
> 99.9%
ValueCountFrequency (%)
-99996
< 0.1%
4.2032 × 10-61
 
< 0.1%
0.00024163631
 
< 0.1%
0.00026542121
 
< 0.1%
0.00026559841
 
< 0.1%
0.00033388491
 
< 0.1%
0.00035752271
 
< 0.1%
0.00036297211
 
< 0.1%
0.00040605161
 
< 0.1%
0.00043487741
 
< 0.1%
ValueCountFrequency (%)
55.150961
< 0.1%
52.32151
< 0.1%
48.506161
< 0.1%
28.03321
< 0.1%
26.415021
< 0.1%
20.709491
< 0.1%
19.052051
< 0.1%
16.700471
< 0.1%
16.665321
< 0.1%
14.793831
< 0.1%

zErr
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2398609
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06009573046
Minimum-9999
Maximum125.6025
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)< 0.1%
Memory size21.1 MiB
2022-02-27T16:54:26.467791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.0093093303
Q10.022616205
median0.06647062
Q30.11302155
95-th percentile0.36526517
Maximum125.6025
Range10124.6025
Interquartile range (IQR)0.090405345

Descriptive statistics

Standard deviation20.80856889
Coefficient of variation (CV)346.2570257
Kurtosis230878.3021
Mean0.06009573046
Median Absolute Deviation (MAD)0.04462266
Skewness-480.4752825
Sum166534.8243
Variance432.9965392
MonotonicityNot monotonic
2022-02-27T16:54:26.565866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999912
 
< 0.1%
0.10690089
 
< 0.1%
0.11966128
 
< 0.1%
0.104168
 
< 0.1%
0.1041018
 
< 0.1%
0.10383198
 
< 0.1%
0.10701118
 
< 0.1%
0.10692928
 
< 0.1%
0.1018178
 
< 0.1%
0.11875778
 
< 0.1%
Other values (2398599)2771074
> 99.9%
ValueCountFrequency (%)
-999912
< 0.1%
0.00017186311
 
< 0.1%
0.000204251
 
< 0.1%
0.00022468031
 
< 0.1%
0.00045838071
 
< 0.1%
0.00047351351
 
< 0.1%
0.00097596651
 
< 0.1%
0.0010065591
 
< 0.1%
0.0010185741
 
< 0.1%
0.0010790471
 
< 0.1%
ValueCountFrequency (%)
125.60251
< 0.1%
70.744061
< 0.1%
51.205181
< 0.1%
48.380921
< 0.1%
43.065531
< 0.1%
38.727211
< 0.1%
35.620291
< 0.1%
33.705261
< 0.1%
28.623171
< 0.1%
28.584331
< 0.1%

Interactions

2022-02-27T16:54:06.914922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:24.175147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:34.913513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:45.093051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:55.300206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:05.935601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:16.088296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:26.263464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:36.425689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:46.604505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:56.718261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:07.835972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:25.160557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:35.842579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:46.020459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:56.277308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:06.865238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:17.025856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:27.191903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:37.369889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:47.533534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:57.663699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:08.754448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:26.134463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:36.752362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:46.936609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:57.236793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:07.793836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:17.940176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:28.117665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:38.298336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:48.446114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:58.577071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:09.683481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:27.125750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:37.684189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:47.875556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:58.196699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:08.722126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:18.884223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:29.047506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:39.225487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:49.370614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:59.521036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:10.622473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:28.101329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:38.610817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:48.789769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:59.173545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:09.633406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:19.800011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:29.976118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:40.138734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:50.300561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:00.447080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:11.538857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:29.076536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:39.539509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:49.717196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:00.153773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:10.561182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:20.723139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:30.889133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:41.069828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:51.214767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:01.361645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:12.467357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:30.061983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:40.465268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:50.631452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:01.122074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:11.474030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:21.637682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:31.799473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:41.999656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:52.127504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:02.289919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:13.380539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:31.044343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:41.379821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:51.557225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:02.082848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:12.403788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:22.565972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:32.728382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:42.907116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:53.052227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:03.218321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:14.293555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:32.001406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:42.308064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:52.470367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:03.058472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:13.313983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:23.478941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:33.642537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:43.819894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:53.950993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:04.128942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:15.236282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:32.993857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:43.236814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:53.399315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:04.033657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:14.244259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:24.400611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:34.578343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:44.748926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:54.864505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:05.057432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:16.180265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:33.969333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:44.146951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:52:54.327525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:04.995078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:15.156783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:25.319035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:35.496802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:45.674756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:53:55.796021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:54:05.970284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-27T16:54:26.643991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-27T16:54:26.753366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-27T16:54:26.862741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-27T16:54:26.972116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-27T16:54:16.345153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-27T16:54:17.453860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IDugrizuErrgErrrErriErrzErr
0123767112912568392024.7015222.3056021.7447421.4008021.423480.7816860.0989800.0782200.0823700.240668
1123765777231439497822.6739721.9668621.9489921.7237921.861050.3908180.0776080.1287820.1721280.565489
2123766076483240050326.1858622.4151621.6536121.5735521.896040.3711850.1113130.0796660.1036730.496207
3123766558433457054624.6047121.8725621.6543221.6429122.185190.8269980.0607780.0652660.1001260.507707
4123765719090430005925.1216422.7999622.2114621.8542522.102070.6820360.1339500.1111800.1120900.480457
5123766354314487476924.0921022.8534622.3076322.0479022.402830.6423210.1290140.1242500.1358430.529423
6123768050343346338123.9805424.2461822.7301321.3028819.857081.7011480.9327390.4797800.1942790.188464
7123766829821027949624.1753326.8435220.1388918.7263621.075542.5584260.6756040.0615690.0603170.877250
8123765787700074992021.8025020.7598820.2154620.1375420.198180.1385950.0319380.0255080.0320240.116473
9123766835028433354125.3014921.9747421.2539121.1610821.161520.6452500.0788750.0527450.0676740.213721

Last rows

IDugrizuErrgErrrErriErrzErr
2771149123768010024078618823.0586922.3616821.5330321.1167020.319470.6016640.1368260.1198760.1135210.223924
2771150123766230297811400521.7140321.3723920.9356320.7153620.445280.1256910.0412730.0406240.0468670.112831
2771151123765861197702850024.8257123.1388621.5402620.3089519.378262.8694780.5842350.2025270.0991330.153437
2771152123766558432709866319.9325219.6159419.4306319.4592019.334380.0395150.0130050.0135620.0194020.055167
2771153123767879081728422420.0775419.6628719.6089819.6364720.096040.0559200.0145540.0196430.0271990.162892
2771154123766970105115466921.6929121.8026821.3754721.3653921.507890.2055820.0915140.1093650.1641780.592550
2771155123765153548383051321.1295221.3159621.4913421.1873620.980130.1024430.0561570.0891180.1017490.310130
2771156123768031122980945022.0768521.7965721.4865921.4014620.842150.2322130.0568640.0763200.1003190.263959
2771157123766146383622995221.2660120.7593120.5711120.4226220.526660.0857250.0247490.0283260.0337300.121159
2771158123764992004358185222.5910921.2713920.8219920.6160720.615850.3059510.0481920.0457080.0556660.222432

Duplicate rows

Most frequently occurring

IDugrizuErrgErrrErriErrzErr# duplicates
0123764992004666177118.9213817.1525616.1133815.5898615.171780.0536630.0064820.0043570.0041120.0097622
1123765366471812732320.0343117.9715416.8541716.3972315.945990.0678210.0071780.0046250.0046750.0104482
2123765510677181248919.9079718.0261717.0122016.6074416.262830.0540200.0073590.0048230.0046890.0096392
3123765536983148138818.8747616.7392315.5990314.9962414.481760.0530570.0049540.0033370.0031230.0064692
4123765537357784717817.2847216.3155015.8149515.5291715.287230.0116110.0034950.0031240.0033240.0070572
5123765707008861823023.0401021.7148721.5409722.2368222.602380.5795440.0781900.0986340.2609000.8155912
6123765707169811668518.3634617.0228616.5942816.3906416.241010.0719540.0127280.0171250.0214920.0499752
7123765758709622409619.9591618.9179718.4433218.2411118.088430.0755550.0171540.0157530.0291330.0566062
8123765759139315732217.8524516.4139615.8531815.4164315.119330.0184820.0038510.0035450.0041670.0067362
9123765785768008109719.1262717.8344017.0834616.6076016.304090.0322530.0062890.0050090.0051820.0110972